As content providers have moved to broaden and deepen their own news and other media offerings, they have accordingly sought ways to improve efficiency and minimize the cost of content generation. Content generation produced in a narrative language context is particularly difficult and costly to provide, but highly desired by clients of systems that produce news and other text media offerings. Building a framework for automatic generation of narrative language text from incoming data minimizes the need for human interaction in the creation of narrative language text, and presents a cost efficient method for the transformation of data into narrative language text.
The transformation of data into one or more automatically generated narrative language articles is subject to the identification of the data that is of interest to consumers of that data. After the identification of data of interest, the narrative generation must be structured to provide text reflecting the maximum flexibility, breadth, and variation that the data encompasses.